Massively Multitask Networks for Drug Discovery

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Abstract

Massively multitask neural architectures provide a learning framework for drug
discovery that synthesizes information from many distinct biological sources. To
train these architectures at scale, we gather large amounts of data from public
sources to create a dataset of nearly 40 million measurements across more than 200
biological targets. We investigate several aspects of the multitask framework by
performing a series of empirical studies and obtain some interesting results: (1)
massively multitask networks obtain predictive accuracies significantly better than
single-task methods, (2) the predictive power of multitask networks improves as
additional tasks and data are added, (3) the total amount of data and the total
number of tasks both contribute significantly to multitask improvement, and (4)
multitask networks afford limited transferability to tasks not in the training set.
Our results underscore the need for greater data sharing and further algorithmic
innovation to accelerate the drug discovery process.